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Learner Reviews & Feedback for State Estimation and Localization for Self-Driving Cars by University of Toronto

4.7
stars
807 ratings

About the Course

Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to: - Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares - Develop a model for typical vehicle localization sensors, including GPS and IMUs - Apply extended and unscented Kalman Filters to a vehicle state estimation problem - Understand LIDAR scan matching and the Iterative Closest Point algorithm - Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws)....

Top reviews

GN

Oct 29, 2019

best online course so far that explains kalman filter and estimation methods with examples not just focusing on theoretical ,Thanks to the Dr's and course staff who worked hard to produce this course.

WS

Oct 13, 2019

There are many interesting topics. Without the help and suggested readings from this course, I wouldn't be able to finish by myself. Also, the final project is very enlightening.

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76 - 100 of 129 Reviews for State Estimation and Localization for Self-Driving Cars

By DHAYAPULLESAIPAVAN R

May 26, 2021

nice but,some part are not clearly explained

By José C I G Z

Dec 9, 2020

Awesome course and very challenging!

By 刘宇轩

Apr 25, 2019

The projects are useful enough

By shridhar v

Jun 8, 2020

Kalman filter was interesting

By 张志萌

Mar 8, 2022

very good course. Thank you

By Vishwas N

Apr 29, 2020

very nicely crafted course

By Johnnylee

May 16, 2022

课程内容很充实又有意义,希望有准确的中文字幕

By Seyed M A S

Mar 1, 2024

It was a great course

By Luis E T R

Sep 13, 2020

An outstanding course

By Sujeet B

Dec 7, 2020

Lot's of learning...

By Davi F d B

Sep 25, 2022

Excelente curso!

By Bao N

May 28, 2020

Many many thanks!

By MIHIR R J

May 30, 2020

Very Infomative!

By mert s

Oct 31, 2019

excellent course

By Satwik C

Nov 4, 2023

Good course

By Adam A A

Mar 2, 2021

just pefect

By Arturo A E O

Sep 2, 2020

excelente

By Cesar Q

Oct 15, 2021

Love it!

By Matías F

Jan 18, 2021

amazing!

By Jeff D

Nov 28, 2020

Thanks

By Md. R Q S

Aug 19, 2020

great

By 01fe21bec413

Mar 21, 2024

Good

By Soumyajit M

Oct 5, 2020

Good

By Nejc D

May 6, 2020

The course covers some interesting and highly important concepts regarding state estimation. I guess the videos are not intended to be a "follow-along" lectures but more of a "these are the topics you should study by yourself" videos. In other words, the videos tend not to go deep, instead only the important results are quickly presented. On the other hand the programming assigments are quite fun.

Reflecting on how much knowledge and understanding somebody needs to show to pass this course I wouldn't rate it as advanced, I would rather say intermediate.

To sum up, this is either a course for somebody who wants to get some basic ideas about state estimation applied to self driving cars or for somebody who wants to dive deep into this topic and wants to use this course as a guidance on his/her self-study journey

By Maksym B

Apr 3, 2019

The course has very advanced material and I value this course a lot. However I am very confused at some key concepts and didn't understand many details conceptually. For example it is not clear what is the difference between EKF and ES-EKF.

Also, for the final project the formulas have been given. I implemented the project using the formulas, but I didn't understand deeply enough the meaning of those formulas. For example what does Kalman Gain represent.

Maybe the topic is just so advanced, or maybe I should be reading more resources outside the lectures. But I finished the course with the feeling that I have a lot to learn in the space of localization and state estimation.